{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.  7.5 8.  0.  1. ]\n",
      "float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import numpy as np\n",
    "\n",
    "# 使用普通一维数组生成NumPy一维数组\n",
    "data = [6, 7.5, 8, 0, 1]\n",
    "arr = np.array(data)\n",
    "print(arr)\n",
    "print(arr.dtype) #类型\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [5 6 7 8]]\n",
      "(2L, 4L)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用普通二维数组生成NumPy二维数组\n",
    "data = [[1, 2, 3, 4], [5, 6, 7, 8]]\n",
    "arr = np.array(data)\n",
    "print(arr)\n",
    "print(arr.shape) # 维度\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[[0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0.]]\n",
      "[[[2.30617037e-316 2.07470615e-316]\n",
      "  [0.00000000e+000 0.00000000e+000]\n",
      "  [0.00000000e+000 0.00000000e+000]]\n",
      "\n",
      " [[0.00000000e+000 0.00000000e+000]\n",
      "  [0.00000000e+000 0.00000000e+000]\n",
      "  [0.00000000e+000 0.00000000e+000]]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用zeros/empty\n",
    "print(np.zeros(10)) # 生成包含10个0的一维数组\n",
    "print(np.zeros((3,6))) # 生成3*6的二维数组\n",
    "print(np.empty((2,3,2))) # 生成2*3*2的三维数组,所有元素未初始化。\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "# 使用arrange生成连续元素\n",
    "print(np.arange(15)) # [0,1,2, ..., 14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 8 9]\n",
      "3\n",
      "3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 通过索引访问二维数组某一行或某个元素\n",
    "arr = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
    "print(arr[2])\n",
    "print(arr[0][2])\n",
    "print(arr[0, 2]) # 普通Python数组不能用。\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "[7 8 9]\n",
      "[[[42 42 42]\n",
      "  [42 42 42]]\n",
      "\n",
      " [[ 7  8  9]\n",
      "  [10 11 12]]]\n",
      "[[[ 1  2  3]\n",
      "  [ 4  5  6]]\n",
      "\n",
      " [[ 7  8  9]\n",
      "  [10 11 12]]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 对更高维数组的访问和操作\n",
    "arr = np.array([[[1, 2, 3],[4, 5, 6]],[[7, 8, 9],[10, 11, 12]]])\n",
    "print(arr[0])  # 结果是个2维数组\n",
    "print(arr[1, 0]) # 结果是个2维数组\n",
    "old_values = arr[0].copy() # 复制arr[0]的值\n",
    "arr[0] = 42 # 把arr[0]所有的元素都设置\n",
    "print arr\n",
    "arr[0] = old_values #把原来的数组写回去\n",
    "print(arr)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5 6]\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "[[2 3]\n",
      " [5 6]]\n",
      "[[1]\n",
      " [4]\n",
      " [7]]\n",
      "[[1 0 0]\n",
      " [4 0 0]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "# 使用切片访问和操作数组\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "print(arr[1:6]) # 打印元素arr[1]到arr[5]，和list切片不同，这里是引用。如果想改变 最好先copy\n",
    "arr = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])\n",
    "print(arr[:2]) #打印第1,2行\n",
    "print(arr[:2,1:])  # 打印第1、2行，第2、3列\n",
    "print(arr[:,:1]) #打印第1列的所有元素\n",
    "arr[:2,1:] = 0 # 第1、2行，第2、3列的元素设置为0\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2L, 500L)\n"
     ]
    }
   ],
   "source": [
    "# 矩阵的计算优化\n",
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "import time\n",
    "\n",
    "X = np.array([range(0,500),range(500,1000)])\n",
    "m, n = X.shape\n",
    "print(m, n)\n",
    "#print X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.19799995422\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "xi与xj为列向量\n",
    "D(i,j) = ||xi - xj||^2\n",
    "'''\n",
    "t = time.time()\n",
    "D = np.zeros([n,n])\n",
    "for i in range(n):\n",
    "    for j in range(i + 1, n):\n",
    "        D[i, j] = la.norm(X[:, i] - X[:,j]) ** 2\n",
    "        D[j, i] = D[i, j]\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.582000017166\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "|xi - xi| = sqrt((xi - xj) * (xi - xj).T)\n",
    "D(i, j) = (xi - xj) * (xi - xj).T\n",
    "'''\n",
    "t = time.time()\n",
    "D = np.zeros([n,n])\n",
    "for i in range(n):\n",
    "    for j in  range(i+1, n):\n",
    "        d = X[:, i] - X[:,j]\n",
    "        D[i, j] = np.dot(d, d)\n",
    "        D[j, i] = D[i, j]\n",
    "print(time.time() -t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.260999917984\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "D(i, j) = (xi - xj) * (xi - xj).T\n",
    "        = xi * xi.T - xi * xj.T - xj * xi.T + xj * xj.T\n",
    "        = xi * xi.T - 2 * xi * xj.T + xj * xj.T\n",
    "G(i,j) = xi.T * xj\n",
    "'''\n",
    "t = time.time()\n",
    "G = np.dot(X.T, X)\n",
    "D = np.zeros([n, n])\n",
    "for i in range(n):\n",
    "    for j in range(i + 1, n):\n",
    "        D[i, j] = G[i, i] - G[i, j] * 2 + G[j, i]\n",
    "        D[j, i] = D[i, j]\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00999999046326\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "H(i, j) = G(i, i)\n",
    "K(i, j) = G(j, j) = H(i, j).T\n",
    "D(i, j) = H(i, j) + K(i, j) - 2 * G(i, j)\n",
    "'''\n",
    "t = time.time()\n",
    "G = np.dot(X.T, X)\n",
    "H = np.tile(np.diag(G), (n, 1)) # n rows, 1 for each row\n",
    "D = H + H.T -G * 2\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用数组生成Series\n",
      "0    4\n",
      "1    7\n",
      "2   -5\n",
      "3    3\n",
      "dtype: int64\n",
      "[ 4  7 -5  3]\n",
      "RangeIndex(start=0, stop=4, step=1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series\n",
    "\n",
    "print '用数组生成Series'\n",
    "obj = Series([4, 7, -5, 3])\n",
    "print obj\n",
    "print obj.values\n",
    "print obj.index\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指定Series的index\n",
      "d    4\n",
      "b    7\n",
      "a   -5\n",
      "c    3\n",
      "dtype: int64\n",
      "Index([u'd', u'b', u'a', u'c'], dtype='object')\n",
      "-5\n",
      "c    3\n",
      "a   -5\n",
      "d    6\n",
      "dtype: int64\n",
      "d    6\n",
      "b    7\n",
      "c    3\n",
      "dtype: int64\n",
      "True\n",
      "False\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '指定Series的index'\n",
    "obj2 = Series([4, 7, -5, 3],index = ['d', 'b', 'a', 'c'])\n",
    "print obj2\n",
    "print obj2.index\n",
    "print obj2['a']\n",
    "obj2['d'] = 6\n",
    "print obj2[['c', 'a', 'd']]\n",
    "print obj2[obj2 > 0]  # 找出大于0的元素\n",
    "print 'b' in obj2 # 判断索引是否存在\n",
    "print 'e' in obj2\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用字典生成Series\n",
      "Ohio      45000\n",
      "Oregon    16000\n",
      "Texas     71000\n",
      "Utah       5000\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '使用字典生成Series'\n",
    "sdata = {'Ohio':45000, 'Texas':71000, 'Oregon':16000, 'Utah':5000}\n",
    "obj3 = Series(sdata)\n",
    "print obj3\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用字典生成Series，并额外指定index，不匹配部分为NaN.\n",
      "Califonia        NaN\n",
      "Ohio         45000.0\n",
      "Oregon       16000.0\n",
      "Texas        71000.0\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print '使用字典生成Series，并额外指定index，不匹配部分为NaN.'\n",
    "states = ['Califonia', 'Ohio', 'Oregon', 'Texas']\n",
    "obj4 = Series(sdata,index = states)\n",
    "print obj4\n",
    "print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series相加，相同索引部分相加。\n",
      "Califonia         NaN\n",
      "Ohio          90000.0\n",
      "Oregon        32000.0\n",
      "Texas        142000.0\n",
      "Utah              NaN\n",
      "dtype: float64\n",
      "\n",
      "指定Series 及其索引的名字\n",
      "state\n",
      "Califonia        NaN\n",
      "Ohio         45000.0\n",
      "Oregon       16000.0\n",
      "Texas        71000.0\n",
      "Name: population, dtype: float64\n",
      "\n",
      "替换index\n",
      "Bob      4\n",
      "Steve    7\n",
      "Jeff    -5\n",
      "Ryan     3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print 'Series相加，相同索引部分相加。'\n",
    "print obj3 + obj4\n",
    "print\n",
    "\n",
    "print '指定Series 及其索引的名字'\n",
    "obj4.name = 'population'\n",
    "obj4.index.name = 'state'\n",
    "print obj4\n",
    "print\n",
    "\n",
    "print '替换index'\n",
    "obj.index = ['Bob','Steve','Jeff','Ryan']\n",
    "print obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pop   state  year\n",
      "0  1.5    Ohio  2000\n",
      "1  1.7    Ohio  2001\n",
      "2  3.6    Ohio  2002\n",
      "3  2.4  Nevada  2001\n",
      "4  2.9  Nevada  2002\n",
      "   year   state  pop\n",
      "0  2000    Ohio  1.5\n",
      "1  2001    Ohio  1.7\n",
      "2  2002    Ohio  3.6\n",
      "3  2001  Nevada  2.4\n",
      "4  2002  Nevada  2.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# 用字典生成DataFrame，key为列的名字。\n",
    "data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'],\n",
    "        'year':[2000, 2001, 2002, 2001, 2002],\n",
    "        'pop':[1.5, 1.7, 3.6, 2.4, 2.9]}\n",
    "print(DataFrame(data))\n",
    "print(DataFrame(data, columns = ['year', 'state', 'pop'])) #指定列排序\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       year   state  pop debt\n",
      "one    2000    Ohio  1.5  NaN\n",
      "two    2001    Ohio  1.7  NaN\n",
      "three  2002    Ohio  3.6  NaN\n",
      "four   2001  Nevada  2.4  NaN\n",
      "five   2002  Nevada  2.9  NaN\n",
      "one        Ohio\n",
      "two        Ohio\n",
      "three      Ohio\n",
      "four     Nevada\n",
      "five     Nevada\n",
      "Name: state, dtype: object\n",
      "one      2000\n",
      "two      2001\n",
      "three    2002\n",
      "four     2001\n",
      "five     2002\n",
      "Name: year, dtype: int64\n",
      "year     2002\n",
      "state    Ohio\n",
      "pop       3.6\n",
      "debt      NaN\n",
      "Name: three, dtype: object\n",
      "       year   state  pop  debt\n",
      "one    2000    Ohio  1.5  16.5\n",
      "two    2001    Ohio  1.7  16.5\n",
      "three  2002    Ohio  3.6  16.5\n",
      "four   2001  Nevada  2.4  16.5\n",
      "five   2002  Nevada  2.9  16.5\n",
      "       year   state  pop  debt\n",
      "one    2000    Ohio  1.5     0\n",
      "two    2001    Ohio  1.7     1\n",
      "three  2002    Ohio  3.6     2\n",
      "four   2001  Nevada  2.4     3\n",
      "five   2002  Nevada  2.9     4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:8: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# 指定索引，在列中指定不存在的列，默认数据用NaN。\n",
    "frame2 = DataFrame(data,\n",
    "                    columns = ['year', 'state', 'pop', 'debt'],\n",
    "                    index = ['one', 'two', 'three', 'four', 'five'])\n",
    "print(frame2)\n",
    "print(frame2['state'])\n",
    "print(frame2.year)\n",
    "print(frame2.ix['three'])\n",
    "frame2['debt'] = 16.5 #修改一整列\n",
    "print(frame2)\n",
    "frame2.debt = np.arange(5) #用numpy数组修改元素\n",
    "print(frame2)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       year   state  pop  debt\n",
      "one    2000    Ohio  1.5   NaN\n",
      "two    2001    Ohio  1.7  -1.2\n",
      "three  2002    Ohio  3.6   NaN\n",
      "four   2001  Nevada  2.4  -1.5\n",
      "five   2002  Nevada  2.9  -1.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 用Series指定要修改的索引及其对应的值，没有指定的默认数据用NaN。\n",
    "val = Series([-1.2,-1.5,-1.7],index = ['two', 'four', 'five'])\n",
    "frame2['debt'] = val\n",
    "print(frame2)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       year   state  pop  debt  eastern\n",
      "one    2000    Ohio  1.5   NaN     True\n",
      "two    2001    Ohio  1.7  -1.2     True\n",
      "three  2002    Ohio  3.6   NaN     True\n",
      "four   2001  Nevada  2.4  -1.5    False\n",
      "five   2002  Nevada  2.9  -1.7    False\n",
      "Index([u'year', u'state', u'pop', u'debt', u'eastern'], dtype='object')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 赋值给新列\n",
    "frame2['eastern'] = (frame2.state == 'Ohio')   # 如果state等于Ohio为True\n",
    "print(frame2)\n",
    "print(frame2.columns)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Nevada  Ohio\n",
      "2000     NaN   1.5\n",
      "2001     2.4   1.7\n",
      "2002     2.9   3.6\n",
      "        2000  2001  2002\n",
      "Nevada   NaN   2.4   2.9\n",
      "Ohio     1.5   1.7   3.6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DataFrame 转置\n",
    "pop = {'Nevada':{2001:2.4,2002:2.9},\n",
    "        'Ohio':{2000:1.5,2001:1.7,2002:3.6}}\n",
    "frame3 = DataFrame(pop)\n",
    "print(frame3)\n",
    "print(frame3.T)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state  Nevada  Ohio\n",
      "year               \n",
      "2000      NaN   1.5\n",
      "2001      2.4   1.7\n",
      "2002      2.9   3.6\n",
      "[[nan 1.5]\n",
      " [2.4 1.7]\n",
      " [2.9 3.6]]\n",
      "[[2000L 'Ohio' 1.5 nan True]\n",
      " [2001L 'Ohio' 1.7 -1.2 True]\n",
      " [2002L 'Ohio' 3.6 nan True]\n",
      " [2001L 'Nevada' 2.4 -1.5 False]\n",
      " [2002L 'Nevada' 2.9 -1.7 False]]\n"
     ]
    }
   ],
   "source": [
    "# 指定索引和列的名称\n",
    "frame3.index.name = 'year'\n",
    "frame3.columns.name = 'state'\n",
    "print(frame3)\n",
    "print(frame3.values)\n",
    "print(frame2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1400.0, 1306.25, 751.0, 788.0, 1094.0]\n"
     ]
    }
   ],
   "source": [
    "# 读取并计算Excel文件\n",
    "# -*- coding:utf-8 -*-\n",
    "import pandas as pd\n",
    "\n",
    "def tax(s):\n",
    "    ss = s - 3500\n",
    "    if ss <= 0:\n",
    "        return 0\n",
    "    elif ss < 1500:\n",
    "        return ss * 0.03\n",
    "    elif ss < 4500:\n",
    "        return ss * 0.1 - 105\n",
    "    elif ss < 9000:\n",
    "        return ss * 0.2 - 555\n",
    "    elif ss < 35000:\n",
    "        return ss * 0.25 - 1005\n",
    "    elif ss < 55000:\n",
    "        return ss * 0.3 - 2755\n",
    "    elif ss < 8000:\n",
    "        return ss * 0.35 - 5505\n",
    "    else:\n",
    "        return ss * 0.45 - 13505\n",
    "    \n",
    "df = pd.read_excel('./1/salary.xlsx',sheetname = 0)\n",
    "ts = []\n",
    "for s in df[u'工资']:\n",
    "    ts.append(tax(s))\n",
    "print(ts)\n",
    "df[u'税'] = ts\n",
    "\n",
    "out = pd.ExcelWriter('./1/salary_and_tax.xls')\n",
    "df.to_excel(out)\n",
    "out.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  1.798173       NaN       NaN\n",
      "1  0.301539       NaN       NaN\n",
      "2 -0.243238       NaN       NaN\n",
      "3  1.049645       NaN  0.012043\n",
      "4  0.445177       NaN  0.261165\n",
      "5  0.399639 -0.088791 -0.320791\n",
      "6  0.814406 -1.148207  1.039720\n",
      "          0         1         2\n",
      "0  1.798173  0.000000  0.000000\n",
      "1  0.301539  0.000000  0.000000\n",
      "2 -0.243238  0.000000  0.000000\n",
      "3  1.049645  0.000000  0.012043\n",
      "4  0.445177  0.000000  0.261165\n",
      "5  0.399639 -0.088791 -0.320791\n",
      "6  0.814406 -1.148207  1.039720\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if __name__ == '__main__':\n",
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "# 确实数据处理\n",
    "import numpy as np\n",
    "from numpy import nan as NA\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame,Index\n",
    "\n",
    "# 填充0\n",
    "df = DataFrame(np.random.randn(7,3))\n",
    "df.ix[:4, 1] = NA\n",
    "df.ix[:2, 2] = NA\n",
    "print df\n",
    "df.fillna(0, inplace = True)\n",
    "print df\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "          0         1         2\n",
      "0  1.257864  0.500000       NaN\n",
      "1 -0.935324  0.500000       NaN\n",
      "2 -0.979921  0.500000       NaN\n",
      "3 -0.396882  0.500000 -1.003516\n",
      "4 -0.891871  0.500000  2.266216\n",
      "5 -1.195808 -0.031187 -1.757212\n",
      "6 -0.463291  0.934383  1.321464\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# 不同行列填充不同的值\n",
    "df = DataFrame(np.random.randn(7,3))\n",
    "df.ix[:4, 1] = NA\n",
    "df.ix[:2, 2] = NA\n",
    "print(df.fillna({1:0.5, 3:-1},inplace = True)) #第三列不存在\n",
    "print df\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2\n",
      "0  0.433773  0.503924 -0.787175\n",
      "1 -1.108038 -0.675707 -1.127486\n",
      "2 -0.981808       NaN -0.182870\n",
      "3  0.883969       NaN -0.371312\n",
      "4 -0.042251       NaN       NaN\n",
      "5 -0.830846       NaN       NaN\n",
      "          0         1         2\n",
      "0  0.433773  0.503924 -0.787175\n",
      "1 -1.108038 -0.675707 -1.127486\n",
      "2 -0.981808 -0.675707 -0.182870\n",
      "3  0.883969 -0.675707 -0.371312\n",
      "4 -0.042251 -0.675707 -0.371312\n",
      "5 -0.830846 -0.675707 -0.371312\n",
      "          0         1         2\n",
      "0  0.433773  0.503924 -0.787175\n",
      "1 -1.108038 -0.675707 -1.127486\n",
      "2 -0.981808 -0.675707 -0.182870\n",
      "3  0.883969 -0.675707 -0.371312\n",
      "4 -0.042251       NaN -0.371312\n",
      "5 -0.830846       NaN -0.371312\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "F:\\Program\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# 不同的填充方式\n",
    "df = DataFrame(np.random.randn(6,3))\n",
    "df.ix[2:, 1] = NA\n",
    "df.ix[4:, 2] = NA\n",
    "print(df)\n",
    "print(df.fillna(method='ffill'))#\n",
    "print(df.fillna(method='ffill',limit = 2))\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.000000\n",
      "1    3.833333\n",
      "2    3.500000\n",
      "3    3.833333\n",
      "4    7.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 用统计数据进行填充\n",
    "data = Series([1., NA, 3.5, NA, 7])\n",
    "print(data.fillna(data.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# 基本配置\n",
    "plt.figure(figsize = (10, 10), dpi = 80) #大小和分辨率\n",
    "plt.xlim(-4.0,4.0) # 坐标上下限\n",
    "plt.ylim(-1.0,1.0)\n",
    "'''\n",
    "plt.xticks(np.linspace(-4, 4, 9, endpoint = True))\n",
    "plt.yticks(np.linspace(-1, 1, 5, endpoint = True))\n",
    "\n",
    "'''\n",
    "# 更直观的记号\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],[r'$-\\pi$',r'$-\\pi/2$',r'$0$',r'$+\\pi/2$',r'$+\\pi$'])\n",
    "plt.yticks([-1,0,+1],[r'$-1$','$0$','$+1$'])\n",
    "\n",
    "# 画曲线\n",
    "X = np.linspace(-np.pi, np.pi, 256, endpoint = True)\n",
    "Cos,Sin = np.cos(X), np.sin(X)\n",
    "plt.plot(X, Cos, color = 'blue', linewidth = 1.0, linestyle = '-', label = 'cos') # label 添加图例\n",
    "plt.plot(X, Sin, color = 'green', linewidth = 1.0,linestyle = '-',label = 'sin')\n",
    "plt.legend(loc = 'upper left') #图例位置在左上角\n",
    "\n",
    "# 移动坐标\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data',0))\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "\n",
    "# 给特殊点加注释\n",
    "t = 2*np.pi/3\n",
    "plt.plot([t,t], [0,np.cos(t)], color = 'blue', linewidth = 2.5, linestyle = '--')\n",
    "plt.scatter([t,], [np.cos(t),], 50, color = 'red')\n",
    "plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
    "            xy = (t, np.cos(t)), xycoords = 'data',\n",
    "            xytext = (-90, -50), textcoords = 'offset points', fontsize = 16,\n",
    "            arrowprops = {'arrowstyle':'->','connectionstyle':'arc3,rad=.2'})\n",
    "plt.plot([t,t], [0,np.sin(t)], color = 'red', linewidth = 2.5, linestyle = '--')\n",
    "plt.scatter([t,], [np.sin(t),], 50, color = 'red')\n",
    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n",
    "            xy = (t, np.sin(t)), xycoords = 'data',\n",
    "            xytext = (+10, +30), textcoords = 'offset points', fontsize = 16,\n",
    "            arrowprops = {'arrowstyle':'->','connectionstyle':'arc3,rad=.2'})\n",
    "\n",
    "# 显示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "X = np.arange(-4, 4, 0.25)\n",
    "Y = np.arange(-4, 4, 0.25)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X ** 2 + Y ** 2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.cm.hot )\n",
    "ax.contourf(X, Y, Z, zdir = 'z', offset = -2, cmap = plt.cm.hot)\n",
    "ax.set_zlim(-2, 2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n = 12\n",
    "X = np.arange(n)\n",
    "Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "\n",
    "plt.axes([0.025, 0.025, 0.95, 0.95])\n",
    "plt.bar(X, +Y1, facecolor = '#9999ff', edgecolor = 'white')\n",
    "plt.bar(X, -Y2, facecolor = '#ff9999', edgecolor = 'white')\n",
    "\n",
    "for x, y in zip(X,Y1):\n",
    "    plt.text(x, y + 0.08, '%.2f' % y, ha = 'center', va = 'bottom')\n",
    "    \n",
    "for x, y in zip(X,Y2):\n",
    "    plt.text(x, -y - 0.08, '%.2f' % y, ha = 'center',va = 'top')\n",
    "    \n",
    "plt.xlim(-.5,n),\n",
    "plt.ylim(-1.25, +1.25),\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n = 1024\n",
    "X = np.random.normal(0, 1, n)\n",
    "Y = np.random.normal(0, 1, n)\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(u'\\u771f\\u5b9e\\u53c2\\u6570:', [10, 0.34, 0.5235987755982988])\n",
      "(u'\\u62df\\u5408\\u53c2\\u6570', array([-10.57139024,   0.34022418,  -2.60171349]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 最小二乘\n",
    "# -*- coding:utf-8 -*-\n",
    "import numpy as np\n",
    "from scipy.optimize import leastsq\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def func(x,p):\n",
    "    \"\"\"\n",
    "    数据拟合所用的函数: A*sin(2*pi*k*x + theta)\n",
    "    \"\"\"\n",
    "    A, k, theta = p\n",
    "    return A * np.sin(2 * np.pi * k * x + theta)\n",
    "\n",
    "def residuals(p, y, x):\n",
    "    \"\"\"\n",
    "    实验数据x, y和拟合函数之间的差，p为拟合需要找到的系数\n",
    "    \"\"\"\n",
    "    return y - func(x,p)\n",
    "\n",
    "x = np.linspace(0, -2 * np.pi, 100)\n",
    "A, k, theta = 10, 0.34, np.pi/6 # 真实数据的函数参数\n",
    "y0 = func(x, [A, k, theta]) # 真实数据\n",
    "y1 = y0 + 2 * np.random.randn(len(x)) # 加入噪声之后的实验数据\n",
    "\n",
    "p0 = [7, 0.2, 0] #第一次猜测的函数拟合参数\n",
    "# 调用leastsq进行数据拟合\n",
    "# residuals为计算误差的函数\n",
    "# p0为拟合参数的初始值\n",
    "# args为需要拟合的实验数据\n",
    "plsq = leastsq(residuals, p0, args = (y1, x))\n",
    "\n",
    "print(u'真实参数:', [A, k, theta])\n",
    "print(u'拟合参数', plsq[0]) # 实验数据拟合后的参数\n",
    "\n",
    "plt.plot(x, y0, label = 'Standard data')\n",
    "plt.plot(x, y1, label = 'Test data with noise')\n",
    "plt.plot(x, func(x, plsq[0]), label = 'Result data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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